{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# HelloWorld"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 你好世界"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "HelloWorld\n",
      "你好世界\n"
     ]
    }
   ],
   "source": [
    "print(\"HelloWorld\")\n",
    "print(\"你好世界\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "array : \n",
      "[[1 2 3]\n",
      " [4 5 6]]\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# numpy\n",
    "\n",
    "import numpy\n",
    "\n",
    "# 数组\n",
    "array = numpy.array([[1, 2, 3], [4, 5, 6]])\n",
    "\n",
    "print(\"array : \\n{}\".format(array))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "array : \n",
      "[[1. 0. 0. 0. 0.]\n",
      " [0. 1. 0. 0. 0.]\n",
      " [0. 0. 1. 0. 0.]\n",
      " [0. 0. 0. 1. 0.]\n",
      " [0. 0. 0. 0. 1.]]\n",
      "matrix : \n",
      "  (0, 0)\t1.0\n",
      "  (1, 1)\t1.0\n",
      "  (2, 2)\t1.0\n",
      "  (3, 3)\t1.0\n",
      "  (4, 4)\t1.0\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# scipy\n",
    "\n",
    "import numpy\n",
    "from scipy import sparse\n",
    "\n",
    "# 单位矩阵\n",
    "array = numpy.eye(5)\n",
    "# 稀疏矩阵\n",
    "matrix = sparse.csr_matrix(array)\n",
    "\n",
    "print(\"array : \\n{}\".format(array))\n",
    "print(\"matrix : \\n{}\".format(matrix))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>username</th>\n",
       "      <th>password</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>cch</td>\n",
       "      <td>cch</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>chench</td>\n",
       "      <td>chench</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>hangcchhn</td>\n",
       "      <td>hangcchhn</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>chenchanghang</td>\n",
       "      <td>chenchanghang</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        username       password\n",
       "0            cch            cch\n",
       "1         chench         chench\n",
       "2      hangcchhn      hangcchhn\n",
       "3  chenchanghang  chenchanghang"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# pandas\n",
    "\n",
    "import pandas\n",
    "\n",
    "data = {\n",
    "    \"username\":[\"cch\", \"chench\", \"hangcchhn\", \"chenchanghang\"],\n",
    "    \"password\":[\"cch\", \"chench\", \"hangcchhn\", \"chenchanghang\"]\n",
    "}\n",
    "\n",
    "frame = pandas.DataFrame(data)\n",
    "\n",
    "display(frame)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0xa22edd0>]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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3L4BI4TCzzkG37sCmqNnygraa2kVEPuWvb+ZyzyurOHdQF/70jRNIS0kOO1KjEbeiYGatgOeB77r77sM8wai6CX6Y9ureayKRXU/06tWr9mFFpEFyd3736kf8+Y1cvjr0KH739aE0S9blVvEUl6VpZs2IFISn3P2fQfOWYLcQwe+tQXse0DNq9h5A/mHaP8PdJ7t7trtnd+rUKR5/gogkOHfnzpkr+PMbuVyS3ZP7LjleBaEOxOPsIwMeAVa4+++jJs0ADp5BNAGYHtV+RXAW0khgV7CbaRZwrpm1Dw4wnxu0iUgTt7u4jFufX8KUd9fxrVMy+fX/Hkdykp6nXBfisfvoVOByYImZLQzafgL8BnjGzK4GNgIXB9NeAsYCucB+4EoAdy8yszuBeUG/X7h7URzyiUgDtXnnAf7+zjqmztvE3pJyvvPlfvzovGM4zO5piZG5V7vbvsHIzs72nJycsGOISBwt3byLh/67lpmLCwA4f0g3rh3Vl8HddafTeDGz+e6efWi7zuESkYTg7rz50TYeenst760pJD01mW+dkslVX+pD93Ytwo7XZKgoiEioSsormLEwn4f+u5aPtuylS5s0bh0zgEuH96JtC12MVt9UFEQkFLv2l/HU3A08+u56tu4pYUDX1vzu4qFcMPQoUlN0VlFYVBREpF5tKtrPlHfXMW3eJvaXVjAqK4PfXjyUUVkZOoCcAFQURKReLM7byeS31/LSkgKSzLhg6FFcO6ovg45qE3Y0iaKiICJ1prLSefOjrUx+ey0frC2iVVoK14zqy7dOyeQoHTxOSCoKIhJ3xWUVTF+4mYf+u47crXvp1rY5t48dyCXDe9KmuQ4eJzIVBRGJm537S3nygw08+t4Gtu8tYVC3NvzhkuP5ypBuuiVFA6GiICIx21j4ycHjA2UVnH50Jyae1pdT+nXUweMGRkVBRI7Ywk07eejttby8tIDkJGPc8d25ZlQfBnTVweOGSkVBRGqlstJ5feVWHnp7LXPXF9G6eQrXnd6Pb52SSZc2zcOOJzFSURCRL6S4rIJ/friZh99Zy9pt++jergV3nD+IS07qqaeeNSL6lxSRw9q6p5ipczfx2HvrKdxXyuDubbj/0hMYO7grKTp43OioKIg0ce5O4b5SNhTuY/32/ZHfhZ/83nWgDIAzB3Tm2lF9Gdm3gw4eN2IqCiJNgLuzdU8J67fvY0PhftYXfvr33pLyqr5JBt3btyCzYzoXDO1G7w7pfPmYTmR1aR3iXyD1RUVBpJGorHQKdhezYXv0N/3ISn9D4X4OlFVU9U1JMnp2aEnvji05KbMDvTu2JLNjOr07tqRH+5a6IV0TlnBFwcxGA38EkoGH3f03IUcSSRjlFZXk7ywOVvaf3s2zsWg/peWVVX1Tk5Po1bElmR1bcmr/DDI7tqR3x3QyO6ZzVLvmOh4g1UqoomBmycBfgHOAPGCemc1w9+XhJhP5YioqnfLKSsornPJKp7yikopKp6zSqahwyiqD8aA90ueTeaKnlVU6RXtLPrXi31S0n/LKT56W2LxZEpkd0+nXKZ2zBnQOVvot6Z2RTtc2zfUcY6m1hCoKwHAg193XApjZVGAcoKIgcVNcVkHhvlIK95YEv0sp2ldC4d7SqvY9xeWRFfkhK/hPVuKRFXlF1HB5pVMXT7dNT02md8d0BnZrzZjBXat282RmpNO5dZoO+kpcJVpR6A5sihrPA0Yc2snMJgITAXr16lU/ySRhFZdVULSvlKJ9pWzfW0JRsKIv3HfIyn5fCUV7S9lXWlHt66SmJNExPZWOrVJp07wZLZOTSEmyyE+ykZKUVDWcnJREs2QjOclolpwU+Z0UaY/0PWTaIfNEv1ZK1DwpwXsenKdti1QyWqVqxS/1JtGKQnWf/M9893L3ycBkgOzs7Dr4biZhKi2vjKzYgxV6dSv7wn2fjEefOROtWbLRIT2VjulpdGyVSmbHlnQIhiMr/zQ6pEdWuh3SU2mVlqKVrzR5iVYU8oCeUeM9gPyQskg9OVBawX9WbmXm4nzeW1NYdV78oVKSIiv5yIo8jZ7tW1at4Ktb2bdprpW8SG0lWlGYB2SZWR9gMzAe+Ea4kaQuFJdV8NZH25i5uIDXV2xhf2kFGa1SGX1sV7q3b/HZb/PpabRpoZW8SF1LqKLg7uVmdiMwi8gpqVPcfVnIsSROSssreSd3GzMXFTB7+Rb2lJTTvmUzxh3fnQuGdGNE3446W0YkZAlVFADc/SXgpbBzSHyUV1Ty3ppCZi7OZ9ayLew6UEab5imMHtyV84cexSn9OurhKyIJJOGKgjR8FZXOnHWFzFxcwCtLP6ZoXymt0lI4Z1AXzh/SjVFZnXTFrEiCUlGQuKisdOZv3MHMRfm8tPRjtu0poUWzZM4a2JnzhxzFl4/pRPNmyWHHFJHPoaIgR8zdWbhpJzMXF/Di4gI+3l1MWkoSZxzTmfOHduPMAZ1pmaqPmEhDov+xUivuzrL83fx7cT4vLi4gb8cBUpOTOO3oDG4dM4CzB3XRA1dEGjD975XP5e6s2rKHmYsKmLk4n/WF+0lJMk7tn8HNZ2Vx7rFdaduiWdgxRSQOVBSkRrlb9zJzcT4zFxeQu3UvSQan9Mvg+tP7cd6xXWmfnhp2RBGJMxUF+ZQNhfuYubiAfy/KZ+XHezCD4ZkdmHDhYMYM7kpGq7SwI4pIHVJREErKK3ji/Q1MX5jPks27ADixd3smXTCIscd1o0ub5iEnFJH6oqLQxOXvPMC3n/qQRZt2MrRHW24fO5CxQ7rRvV2LsKOJSAhUFJqw99Zs56anF1BcVsGDlw1j9OBuYUcSkZCpKDRB7s5D/13Lb15eSZ+MdP52+cn079wq7FgikgBUFJqYvSXl3PLcYl5cUsCYwV259+Khuq5ARKpobdCErNm2l+uemM/abXu5bcwAJp7WV7eiFpFPUVFoIl5Z+jE/fHYRqSlJPHn1CE7pnxF2JBFJQCoKjVxFpfPbV1fxwJtrGNqzHQ98cxhH6cwiEalBTPcvNrN7zWylmS02s3+ZWbuoabeZWa6ZrTKz86LaRwdtuWZ2a1R7HzObY2arzWyamely2RgV7StlwpS5PPDmGi4d3otnrhupgiAihxXrTe1nA4PdfQjwEXAbgJkNIvIozWOB0cBfzSzZzJKBvwBjgEHApUFfgLuB+9w9C9gBXB1jtiZtcd5OLvjTO8xdX8Q9XxvCr//3ONJSdOtqETm8mIqCu7/q7uXB6AdAj2B4HDDV3UvcfR2QCwwPfnLdfa27lwJTgXEWOdp5JvBcMP9jwIWxZGvKps3byEUPvg/Ac9efzNdP6hlyIhFpKOJ5TOEqYFow3J1IkTgoL2gD2HRI+wigI7AzqsBE9/8MM5sITATo1atXzMEbi5LyCn42Yxn/mLuJL/XP4P5LT6CDblonIrXwuUXBzF4DulYz6XZ3nx70uR0oB546OFs1/Z3qt0z8MP2r5e6TgckA2dnZNfZrSvJ3HuDbT85nUd4uvvPlfvzg3GNITtLppiJSO59bFNz97MNNN7MJwPnAWe5+cAWdB0Tvs+gB5AfD1bVvB9qZWUqwtRDdXz7He7nbufEfCygtr+TBy05k9ODqariIyOeL9eyj0cAtwFfdfX/UpBnAeDNLM7M+QBYwF5gHZAVnGqUSORg9IygmbwAXBfNPAKbHkq0pcHcefGsNlz0yh47pqUy/8VQVBBGJSazHFP4MpAGzgytjP3D36919mZk9AywnslvpBnevADCzG4FZQDIwxd2XBa91CzDVzO4CFgCPxJitUdtbUs6Pnl3Ey0s/5ivHdeOei4aQrttViEiM7JM9Pg1Tdna25+TkhB2jXuVu3ct1T+SwvnA/t44ewDWj+uh2FSJSK2Y2392zD23XV8sG5uUlBfzw2UU0b5bME1cP55R+ul2FiMSPikIDUV5Ryb2vruJvb61laM92PHjZMLq11dXJIhJfKgoNQOHeEm76xwLeW1PIN0b0YtIFg3R1sojUCRWFBLdo006+/eR8tu8r5Z6LhvD1bF2dLCJ1R0UhgU2du5GfTl9Gp9ZpPH/9KRzXo23YkUSkkVNRSEDFZZHbVUydt4lRWRncP/4E2ut2FSJSD1QUEszm4HYVi/N2ccMZ/fj+ObpdhYjUHxWFBPJu7nZuCm5X8bfLT+S8Y3V1sojULxWFBBC5XcVa7p21kn6dWvHg5SfSr1OrsGOJSBOkohCyPcVl/OjZxbyyTLerEJHwae0Tor0l5XztgfdYs20ft48dqNtViEjoVBRC9PMZy8jdupe/Xzmc04/uFHYcEZGYn9EsR+jlJQU8Oz+PG87or4IgIglDRSEEW3YXc9u/ljC0R1v+76yssOOIiFRRUahnlZXOD59dRElZJfddcjzNkvVPICKJIy5rJDP7oZm5mWUE42Zm95tZrpktNrNhUX0nmNnq4GdCVPuJZrYkmOd+a6RHXB99bz3/Xb2dO84fRF+ddioiCSbmomBmPYFzgI1RzWOIPIIzC5gIPBD07QBMAkYAw4FJZtY+mOeBoO/B+UbHmi3RrPp4D795ZSVnD+zMpcN1YzsRSTzx2FK4D/gxEP0It3HA4x7xAdDOzLoB5wGz3b3I3XcAs4HRwbQ27v5+8Lzmx4EL45AtYZSUV3Dz1AW0aZ7Cb742RKeeikhCiqkomNlXgc3uvuiQSd2BTVHjeUHb4drzqmlvNH47axUrP97DvRcNJaNVWthxRESq9bnXKZjZa0B1N+G5HfgJcG51s1XT5kfQXlOmiUR2NdGrV6+auiWMd3O389B/13H5yN6cMaBz2HFERGr0uUXB3c+urt3MjgP6AIuCXSE9gA/NbDiRb/rRO817APlB+5cPaX8zaO9RTf+aMk0GJgNkZ2fXWDwSwa79ZfzgmUX07ZTOT8YODDuOiMhhHfHuI3df4u6d3T3T3TOJrNiHufvHwAzgiuAspJHALncvAGYB55pZ++AA87nArGDaHjMbGZx1dAUwPca/LXTuzk9eWML2vSX88ZITaJGqR2iKSGKrq9tcvASMBXKB/cCVAO5eZGZ3AvOCfr9w96Jg+NvAo0AL4OXgp0H714LNvLi4gB+dd4yemiYiDULcikKwtXBw2IEbaug3BZhSTXsOMDheecK2qWg/P52+jOGZHbj+9H5hxxER+UJ0OW0dqKh0vv/MQgz43deH6slpItJg6C6pdeDBt9Ywb/0O7rtkKD07tAw7jojIF6YthThbkreL+2Z/xPlDunHh8Y3qUgsRaQJUFOLoQGkFN09bQKfWafzywuN01bKINDjafRRHv3xpOeu27+Opa0bQtmWzsOOIiNSathTi5D8rt/DkBxu5dlRfTumXEXYcEZEjoqIQB9v3lvDj5xYzoGtrfnDu0WHHERE5Ytp9FCN355bnFrO7uJynrx1JWoquWhaRhktbCjF6eu5GXl+5ldvGDODoLq3DjiMiEhMVhRis2baXO2cuZ1RWBhNOzgw7johIzFQUjlBZRSXfm7aQ5s2S+e3FQ0nSVcsi0gjomMIR+uNrq1mct4sHLxtGlzbNw44jIhIX2lI4AvPWF/HXN3O5+MQejB7cLew4IiJxo6JQS3uKy/jetIX0aN+SSV89Nuw4IiJxpd1HtfSzGcvJ33mAZ68/hVZpWnwi0rhoS6EWXlxcwPMf5nHjmVmc2Lt92HFEROIu5qJgZjeZ2SozW2Zm90S132ZmucG086LaRwdtuWZ2a1R7HzObY2arzWyamaXGmi2eCnYd4Cf/WsLQnu246cz+YccREakTMRUFMzsDGAcMcfdjgd8G7YOA8cCxwGjgr2aWbGbJwF+AMcAg4NKgL8DdwH3ungXsAK6OJVs8VVY6P3x2EWUVlfzhkuNplqwNLBFpnGJdu30b+I27lwC4+9agfRww1d1L3H0dkWc1Dw9+ct19rbuXAlOBcRa5x/SZwHPB/I8BF8aYLW6mvLuOd3ML+en5g+iTkR52HBGROhNrUTgaGBXs9nnLzE4K2rsDm6L65QVtNbV3BHa6e/kh7dUys4lmlmNmOdu2bYvxTzi8lR/v5p5XVnHOoC5cclLPOn0vEZGwfe7pM2b2GtC1mkm3B/O3B0YCJwHPmFlfoLrLe53qi5Afpn+13H0yMBkgOzu7xn6xKp5alMEAAAgTSURBVC6r4OZ/LKRNi2b85n/10BwRafw+tyi4+9k1TTOzbwP/dHcH5ppZJZBB5Jt+9NfqHkB+MFxd+3agnZmlBFsL0f1Dc++sVazasoe/X3kSHVulhR1HRKTOxbr76AUixwIws6OBVCIr+BnAeDNLM7M+QBYwF5gHZAVnGqUSORg9IygqbwAXBa87AZgeY7aYvLN6O4+8s44JJ/fmjGM6hxlFRKTexHr11RRgipktBUqBCcEKfpmZPQMsB8qBG9y9AsDMbgRmAcnAFHdfFrzWLcBUM7sLWAA8EmO2I7Zzfyk/eHYh/Tu34tYxA8OKISJS7yyyDm+4srOzPScnJ26v5+7c8PSHzF6+hX9951QGd28bt9cWEUkUZjbf3bMPbdcJ94d4/sPNvLTkY75/zjEqCCLS5KgoRNlYuJ9J05cyvE8HJp7WN+w4IiL1TkUhUF5RyfefWUhSknHfJceTrIfmiEgTpNt8Bh54cw05G3bwx/HH071di7DjiIiEQlsKwMJNO/nD66v56tCjGHd8jRdSi4g0ek2+KOwvLed70xbSpXUad44bHHYcEZFQNfndR3e9uIL1hft4+pqRtG3ZLOw4IiKhatJbCq8t38LTczYy8bS+nNyvY9hxRERC12SLwrY9Jdzy/GIGdWvD9885Ouw4IiIJoUnuPnJ3fvzcIvaWlDN1/PGkpSSHHUlEJCE0yaJQUekc3bU1Xz6mM1ldWocdR0QkYTTJopCSnMRtutGdiMhnNNljCiIi8lkqCiIiUkVFQUREqsRUFMzseDP7wMwWmlmOmQ0P2s3M7jezXDNbbGbDouaZYGarg58JUe0nmtmSYJ77TQ9EFhGpd7FuKdwD/Nzdjwd+GowDjCHyCM4sYCLwAICZdQAmASOA4cAkM2sfzPNA0PfgfKNjzCYiIrUUa1FwoE0w3BbID4bHAY97xAdAOzPrBpwHzHb3InffAcwGRgfT2rj7+8HjPB8HLowxm4iI1FKsp6R+F5hlZr8lUmBOCdq7A5ui+uUFbYdrz6umvVpmNpHIVgW9evWK7S8QEZEqn1sUzOw1oGs1k24HzgK+5+7Pm9nXgUeAs4Hqjgf4EbRXy90nA5Mh8ozmw/4BIiLyhX1uUXD3s2uaZmaPAzcHo88CDwfDeUDPqK49iOxaygO+fEj7m0F7j2r6f6758+dvN7MNX6RvNTKA7Uc4b11SrtpRrtpRrtpprLl6V9cY6+6jfOB0Iiv2M4HVQfsM4EYzm0rkoPIudy8ws1nAr6IOLp8L3ObuRWa2x8xGAnOAK4A/fZEA7t7pSMObWY67Zx/p/HVFuWpHuWpHuWqnqeWKtShcC/zRzFKAYoL9/MBLwFggF9gPXAkQrPzvBOYF/X7h7kXB8LeBR4EWwMvBj4iI1KOYioK7vwOcWE27AzfUMM8UYEo17TmAHn0mIhKipn5F8+SwA9RAuWpHuWpHuWqnSeWyyJd6ERERbSmIiEgUFQUREanSJIuCmd1rZiuDm/X9y8zaRU27Lbgp3yozO6+ec11sZsvMrNLMsqPaM83sQHDjwYVm9mAi5Aqmhba8DsnxMzPbHLWMxoaVJcgzOlgmuWZ2a5hZopnZ+uDGkwvNLCfEHFPMbKuZLY1q62Bms4ObZc6OOnU97Fyhf7bMrKeZvWFmK4L/izcH7fFfZu7e5H6IXB+REgzfDdwdDA8CFgFpQB9gDZBcj7kGAscQue4jO6o9E1ga4vKqKVeoy+uQjD8Dfhj2ZyvIkhwsi75AarCMBoWdK8i2HshIgBynAcOiP9dEbqh5azB868H/lwmQK/TPFtANGBYMtwY+Cv7/xX2ZNcktBXd/1d3Lg9EP+ORq6nHAVHcvcfd1RK6zGF6PuVa4+6r6er8v6jC5Ql1eCWw4kOvua929FJhKZFlJwN3fBooOaR4HPBYMP0YIN8WsIVfo3L3A3T8MhvcAK4jcHy7uy6xJFoVDXMUnF8rVdMO+RNDHzBaY2VtmNirsMIFEW143BrsEp4Sx6yFKoi2XaA68ambzgxtLJpIu7l4AkZUg0DnkPNES5bOFmWUCJxC5+0Pcl1msVzQnrMPdyM/dpwd9bgfKgacOzlZN/7ies/tFclWjAOjl7oVmdiLwgpkd6+67Q85V58vrU292+JszPgDcGbz/ncDviBT8MNTrcqmlU90938w6A7PNbGXw7VhqljCfLTNrBTwPfNfdd9fFs8gabVHww9zIDyJPgAPOB87yYIccNd/Ir95y1TBPCVASDM83szXA0UDcDhQeSS7qYXlF+6IZzewhYGZd5fgC6nW51Ia75we/t5rZv4js6kqUorDFzLp55D5p3YCtYQcCcPctB4fD/GyZWTMiBeEpd/9n0Bz3ZdYkdx+Z2WjgFuCr7r4/atIMYLyZpZlZHyJPgJsbRsZoZtbJzJKD4b5Ecq0NNxWQQMsr+A9x0P8AS2vqWw/mAVlm1sfMUoHxRJZVqMws3cxaHxwmcsJFmMvpUDOAg4/onQDUtIVarxLhs2WRTYJHgBXu/vuoSfFfZmEeUQ/xSH4ukX2+C4OfB6Om3U7kzJFVwJh6zvU/RL5llgBbgFlB+9eAZUTOYvkQuCARcoW9vA7J+ASwBFgc/EfpFvJnbCyRM0TWENkFF1qWqEx9g8/QouDzFFou4B9EdouWBZ+tq4GOwOtE7rb8OtAhQXKF/tkCvkRk99XiqPXW2LpYZrrNhYiIVGmSu49ERKR6KgoiIlJFRUFERKqoKIiISBUVBRERqaKiICIiVVQURESkyv8HxIkq1+pvYi0AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# matplotlib\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "from matplotlib import pyplot\n",
    "import numpy\n",
    "\n",
    "x = numpy.linspace(-20, 20, 10)\n",
    "y = x**3 + 2 * x ** 2 + 6 * x + 5\n",
    "\n",
    "pyplot.plot(x, y)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# scikit-learn\n",
    "\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (Spyder)",
   "language": "python3",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
